Efficient Evacuation Plan Research Articles

Multi-period human evacuation model for passenger ships under walking speed uncertainty

An effective evacuation model can steer passengers from unsafe zones to safety in a time-sensitive manner on passenger ships. The International Maritime Organization highly recommends constructing robust evacuation models rooted in understanding human behavior and ship design. This study addresses the challenge by focusing on walking speed, a key determinant of human behavior, the capacity of transitional points (e.g., exit stairs), and the distance of passengers to the exit stairs for a safe and rapid evacuation. This study presents a multi-period human evacuation model aimed at optimizing the evacuation time of the slowest passenger, identifying the optimal number of stairs, and planning route evacuation under passenger walking speed uncertainty affected by the heeling angle for a deck of a passenger ship.A robust optimization approach is also employed to manage uncertainty by defining uncertainty sets for passengers’ walking speeds. The computation of the price of robustness spans various test problems at various levels of conservatism. This research offers multiple managerial implications for maritime industry leaders, paving the way for more efficient evacuation planning.

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function.

Matrix scenario-based urban flooding damage prediction via convolutional neural network

This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess of convolutional neural networks to advance the field of hazard assessment in urban pluvial flooding scenarios. The tool uniquely accounts for the high heterogeneity of urban space and the potential impact of complex climate scenarios, which are often underestimated by traditional data-reliant methods. Employing Shenzhen as a case study, the model showcased superior accuracy, resilience, and interpretability, illuminating potential flood hazards. The performance analysis shows that the model can accurately predict the vast majority of urban flood depths, but has errors in extreme flood predictions (depths greater than 35 cm). Findings underscore escalating flood impacts under enhanced scenario loads, with western and central Shenzhen—regions rife with construction—highlighted as particularly vulnerable. Under the most severe matrix scenario (Scenario 25), economic losses are estimated to be about $25,484 million. These commercial and residential hotspots are anticipated to suffer maximum economic loss, with these two areas accounting for 39.6% and 25.1% of the total losses, necessitating reinforced mitigation efforts, especially during extreme rainfall events and high soil saturation levels. In addition, the flooding control strategies should prioritize the reduction of flood inundation areas and integrate functionally oriented land use characteristics in their development. By aiding in the precise identification of flood-prone areas, this research expedites the development of efficient evacuation plans, bolsters urban sustainability, and augments climate resilience, ultimately mitigating flood-induced economic tolls.

A joint demand and supply management approach to large scale urban evacuation planning: Evacuate or shelter-in-place, staging and dynamic resource allocation

Urban evacuation management is challenging to implement as it requires planning and coordination over a large geographical area. To address these challenges and to bolster evacuation planning and management, joint supply and demand management strategies should be considered. In this study, we explore and jointly optimize evacuate or shelter-in-place (SIP), dynamic resource allocation (DRA), and staging decisions for an efficient evacuation plan that minimizes total risk exposure of the population threatened by a sudden onset disaster. We introduce a Cell Transmission Model-based mathematical formulation and propose an exact solution methodology based on Benders decomposition. We further enhance the effectiveness of the algorithm by solving the Benders subproblem using a network flow based formulation on a time-expanded-network, and generating valid inequalities based on DRA decisions and for time-feasible solutions and develop an effective branch-and-cut algorithm to solve the master problem. We conduct extensive numerical experiments using realistic instances to test the effectiveness of the algorithm and to derive managerial insights. We find that considering evacuate or SIP, staging, and DRA decisions jointly contributes significantly to the effectiveness of the evacuation operations. A zone-based approach where some zones are ordered to evacuate while others shelter-in-place is superior to other approaches where an evacuate or SIP decision is given for all population at risk.

Route Dominance on Symmetric Type Transit-based Evacuation Network

The selection of dominant routes for transit-based evacuation planning problems mainly depends upon the structure and nature of the available network. To achieve efficient evacuation planning, the problem is not only on the selection of a route and a shelter for each route on the available network topology but also on the route-to-vehicle assignment and vice versa, which is more complex and challenging in real practice. The evacuation network adds complexity to the solution and impacts the problem. In this paper, the transit-based evacuation network has been revised from different perspectives focusing on the symmetric type networks with their dominant evacuation planning routes to have the minimum evacuation cost.

A GIS-Based Evacuation Route Planning in Flood-Susceptible Area of Siraha Municipality, Nepal

Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optimal routes to minimize fatalities. However, the information is limited to flood hazard mapping only. This study provides a comprehensive flood susceptibility and evacuation route mapping in the Siraha Municipality of Nepal where a lot of flood events have occurred in the past and are liable to happen in the future. The flood susceptibility map was created using a Geographic Information System (GIS)-based Analytical Hierarchy Process (AHP) over nine flood conditioning factors. It showed that 47% of the total area was highly susceptible to flood, and the remaining was in the safe zone. The assembly points where people would gather for evacuation were selected within the susceptible zone through manual digitization while the emergency shelters were selected within a safe zone such that they can host the maximum number of people. The network analysis approach is used for evacuation route mapping in which the closest facility analysis proposed the optimum evacuation route based on the walking speed of evacuees to reach the emergency shelter place considering the effect of slope and flood on the speed of the pedestrian. A total of 12 out of 22 suggested emergency shelters were within 30 min, 7 within 60 min, and 2 within 100 min walk from the assembly point. Moreover, this study suggests the possible areas for further shelter place allocations based on service area analysis. This study can support the authorities’ decision-making for the flood risk assessment and early warning system planning, and helps in providing an efficient evacuation plan for risk mitigation.

A Simulation-Based Model for Evacuation Demand Estimation under Unconventional Metro Emergencies

When metro networks are shut down due to an unconventional emergency, numerous passengers will get stranded and wait to be evacuated. A clear understanding of the stranded passengers in the network is the basis for designing an efficient evacuation strategy. Previous studies focused more on disruption management and assumed a known evacuation demand. Few studies considered the distribution of stranded passengers, especially when the metro network is shut down due to unconventional emergencies. This motivates us to develop a discrete event-based simulation model to estimate the number and distribution of stranded passengers. The proposed model takes the numbers of stranded passengers at stations and trains as the state variables and develops the state evolution rule of the metro system. The numbers and destinations of stranded passengers at trains and on stations at each time step, including the transfer and non-transfer stations, are calculated to describe the state evolution of the metro system. Chongqing Rail Transit (CRT) network is taken as an example. The result shows that the stations with the most stranded passengers are located downtown. Origin-Destinations (ODs) with more stranded passengers have a relatively shorter travel distance, while ODs with fewer stranded passengers have a relatively long travel distance. The average directional disequilibrium factor of stranded passengers of ODs in the whole network is high, especially for those ODs with many stranded passengers. These findings reveal the distribution characteristics of stranded passengers and can provide significant assistance to an emergency manager who is designing an efficient emergency evacuation plan.

Optimal mass evacuation planning for electric vehicles before natural disasters

The electric vehicle (EV) market has significantly expanded because EVs have lower operational costs while leaving less environmental footprint than internal combustion engine vehicles. However, EVs also come with drawbacks, including long charging time and short operational ranges. With these drawbacks and limited charging facilities, efficient long-distance EV evacuation management is challenging and has not been properly addressed. Without an efficient evacuation plan, serious congestion could happen at charging facilities, the evacuation process would be excessively long, and thus human lives may be put at risk. This study is motivated to investigate the optimal mass evacuation planning for EVs considering limited charging facilities. A three-stage method is proposed to efficiently approach this problem. A case study of Florida hurricane evacuation is conducted. The method's effectiveness is verified by comparing it with a benchmark. Management insights and policy indications are drawn through sensitivity analysis of key parameters.

The isolated community evacuation problem with mixed integer programming

As awareness of the vulnerability of isolated regions to natural disasters grows, the demand for efficient evacuation plans is increasing. However, isolated areas, such as islands, often have characteristics that make conventional methods, such as evacuation by private vehicle, impractical to infeasible. Mathematical models are conventional tools for evacuation planning. Most previous models have focused on densely populated areas, and are inapplicable to isolated communities that are dependent on marine vessels or aircraft to evacuate. This paper introduces the Isolated Community Evacuation Problem (ICEP) and a corresponding mixed integer programming formulation that aims to minimize the evacuation time of an isolated community through optimally routing a coordinated fleet of heterogeneous recovery resources. ICEP differs from previous models on resource-based evacuation in that it is highly asymmetric and incorporates compatibility issues between resources and access points. The formulation is expanded to a two-stage stochastic problem that allows scenario-based optimal resource planning while also ensuring minimal evacuation time. In addition, objective functions with a varying degree of risk are provided, and the sensitivity of the model to different objective functions and problem sizes is presented through numerical experiments. To increase efficiency, structure-based heuristics to solve the deterministic and stochastic problems are introduced and evaluated through computational experiments. The results give researchers and emergency planners in remote areas a tool to build optimal evacuation plans given the heterogeneous resource fleets available, which is something they have not been previously able to do and to take actions to improve the resilience of their communities accordingly.

Optimal Location Identification for Emergency Evacuation Shelters using the Voronoi Diagram

Recently, the frequency of earthquakes in Korea has increased; thus it is very important to select the location of an emergency shelter. Therefore, this study proposed a method for selecting an emergency shelter location using the Voronoi diagram, which is usefully used to identify the shortest route or divide jurisdictions. First, Yangsan-si, where the earthquake zone is located, was selected as the target site for the study area, and the evacuation facility area was analyzed using the Voronoi polygon considering an evacuation to nearby emergency shelters regardless of administrative districts. Next, examining the vulnerable portions of the study area based on the existing earthquake outdoor evacuation facility data and floating population data, it was confirmed that there was a large regional variation. Finally, the location of a new shelter in Yangsan-si was selected by quantifying the location data of the building where supply was insufficient and the distance between emergency evacuation facilities as weights. The analysis result showed that installing a total of 7 shelters was the most efficient. The results of this study are expected to contribute to selecting the location of a new evacuation facility and establishing an efficient evacuation plan in the future.

Multi-Objective Optimization Using Evolutionary Cuckoo Search Algorithm for Evacuation Planning

Proper emergency evacuation planning is a key to ensuring the safety and efficiency of resources allocation in disaster events. An efficient evacuation plan can save human lives and avoid other effects of disasters. To develop effective evacuation plans, this study proposed a multi-objective optimization model that assigns individuals to emergency shelters through safe evacuation routes during the available periods. The main objective of the proposed model is to minimize the total travel distance of individuals leaving evacuation zones to shelters, minimize the risk on evacuation routes and minimize the overload of shelters. The experimental results show that the Discrete Multi-Objective Cuckoo Search (DMOCS) has better and consistent performance as compared to the standard Multi-Objective Cuckoo Search (MOCS) in most cases in terms of execution time; however, the performance of MOCS is still within acceptable ranges. Metrics and measures such as hypervolume indicator, convergence evaluation and parameter tuning have been applied to evaluate the quality of Pareto front and the performance of the proposed algorithm. The results showed that the DMOCS has better performance than the standard MOCS.

A Novel GPU-Based Approach to Exploit Time-Respectingness in Public Transport Networks for Efficient Computation of Earliest Arrival Time

In static temporal networks, the Earliest Arrival Time (EAT) problem is to calculate the earliest possible time of arrival at a set of vertices from a given source vertex. Applications of the EAT problem include designing efficient evacuation planning in dynamic scenarios, optimal journey planning in transport networks, and optimal flow management in supply chains. There exist several solutions for the EAT problem in the literature, however, there is limited work on GPU-based solutions to leverage the capabilities of the high throughput accelerator for better performance. Further, there is also a need for more efficient methods to process the inherent earliest arrival dependencies in a transport network. In this paper, we propose GPU algorithms for the one-to-all Earliest Arrival Time problem in public transport networks. Its key characteristic is that it is very fast for the best-case networks where all temporal paths are <i>time-respecting</i>. We propose our algorithms in five incremental ways, where the subsequent approaches are the improved version of previous ones. The Selective-check-version is the most improved approach and hence, the key algorithm. It uses shared memory for efficient computations. For the Selective-check version, we observed an average speedup of 6.45 against the Serial Connection-scan algorithm and 2.77 w.r.t. the Edge-version algorithm.

Designing emergency flood evacuation plans using robust optimization and artificial intelligence

This paper offers a new robust mathematical model for designing an efficient flood evacuation plan in disasters. The mathematical model takes a set of potential locations for establishing evacuation shelters with limited capacities. We suggest an innovative model to use helicopters to rescue people from the flood for the first time in this field of knowledge. Besides, the helicopters' capacity and maximum flying time are considered limited to adopt real-world conditions. Our goal is to maximize the number of rescued people and to minimize the total cost. By solving the model, we determine the allocation of people to the shelters, the optimal location of shelters, allocation of the helicopters to the evacuation shelters, and flying path and routes of the helicopters. Since the main parameters of the proposed model are due to uncertainty in real-world situations, we implemented robust optimization to formulate uncertainties. Due to the Np-hardness of the suggested formulation, we offer four algorithms to solve the mathematical model. We enhance the efficiency of the algorithms through a robust design of experiments and assess their performance considering several measures via post hoc analysis. At the end, we implement the robust model on real-world data from 2011 Japan’s destructive tsunami in Ishinomaki city. The results reveal that the model is able to provide promising solutions compared to the classical models and leads to higher rescue rates and lower cost.

Modeling transit-assisted hurricane evacuation through socio-spatial networks

ABSTRACT Increasing intensity and frequency of hurricane events underscores the need for efficient and inclusive evacuation plans, particularly for carless and disabled populations. Hurricane evacuation intrinsically involves both social and spatial processes. People’s decision to evacuate spreads over social networks; once their decisions are made, they flee through spatial transportation networks. This article describes a novel effort to integrate socio-spatial networks into an agent-based evacuation simulation model, taking the Florida Keys in the USA as a study area. In the model, households, as agents, were synthesized from Census data, then connected by a ‘home-workplace-neighborhood’ social network, and registered to a spatial road network. A threshold decision model was used to simulate social contagion of households’ decision to evacuate. The resulting travel demands were input into the TRANSIMS platform to generate on-road traffic. The model analyzed scenarios of automobile-only and public transit-assisted evacuation. The results show that the simulated traffic under the automobile-only scenario aligns with the observed traffic dynamics, which validates our socio-spatially integrated model. Adding public transportation capacity significantly reduces the traffic load and evacuation time, and provides a practical, accessible, and equitable route to safety for low mobility populations.

APPLICATION OF U-NET CONVOLUTIONAL NEURAL NETWORK TO BUSHFIRE MONITORING IN AUSTRALIA WITH SENTINEL-1/-2 DATA

Abstract. This paper aims to define a pipeline architecture for near real-time identification of bushfire impact areas using Geoscience Australia Data Cube (AGDC). A series of catastrophic bushfires from late 2019 to early 2020 have captured international attention with their scale of devastation across four of the most populous states across Australia; New South Wales, Queensland, Victoria and South Australia. The extraction of burned areas using multispectral Sentinel-2 observations are straightforward when no cloud or haze obstruction are present. Without clear-sky observations, precisely locating the bushfire affected regions are difficult to achieve. Sentinel-1 C-band dual-polarized (VH/VV) Synthetic Aperture Radar (SAR) data is introduced to effectively elicit and analyse useful information based on backscattering coefficients, unaffected by adverse weather conditions and lack of sunlight. Burned vegetation results in significant volume scattering; co-/cross-polarised response decreases due to leafless trees, as well as coherence change over fire-disturbed areas; two sensors acquired images in a shortened revisit time over the same effected areas; all of which provided discriminative features for identifying burnt areas. Moreover, applying U-Net deep learning framework to train the recent and historical satellite data leads to an effective pre-trained segmentation model of burnt and non-burnt areas, enabling more timely emergency response, more efficient hazard reduction activities and evacuation planning during severe bushfire events. The advantages of this approach could have profound significance for a more robust, timely and accurate method of bushfire detection, utilising a scalable big data processing framework, to predict the bushfire footprint and fire spread model development.

Balancing Hazard Exposure and Walking Distance in Evacuation Route Planning during Earthquake Disasters

Efficient evacuation planning is important for quickly navigating people to shelters during and after an earthquake. Geographical information systems are often used to plan routes that minimize the distance people must walk to reach shelters, but this approach ignores the risk of exposure to hazards such as collapsing buildings. We demonstrate evacuation route assignment approaches that consider both hazard exposure and walking distance, by estimating building collapse hazard zones and incorporating them as travel costs when traversing road networks. We apply our methods to a scenario simulating the 2016 Gyeongju earthquake in South Korea, using the floating population distribution as estimated by a mobile phone network provider. Our results show that balanced routing would allow evacuees to avoid the riskiest districts while walking reasonable distances to open shelters. We discuss the feasibility of the model for balancing both safety and expediency in evacuation route planning.

Flood Evacuation Mapping Using a Time–Distance cartogram

When flooding occurs, people should be evacuated safely to designated shelters along the optimal routes to minimize serious damages on lives and properties. However, in general, only limited information related to evacuation procedures and using a directional arrow to indicate existing shelters is provided on the evacuation map. Moreover, the evacuation routes leading to nearby shelters are not presented effectively to people in an emergency situation. This paper aimed to provide an approach to generate a flood evacuation cartogram based on an actual evacuation. The proposed time–distance cartogram preserves the topological characteristics by minimizing distortion in transforming the evacuation routes. To empirically evaluate its application, we applied the proposed method to Siheung city in Korea. As a result, optimal shelter and evacuation routes were derived by considering significant factors influencing the actual access to the facilities. Moreover, the flood evacuation cartogram provides a more intuitive visualization than classic topographic maps, by relocating shelters and reshaping the routes intended for evacuation. The suggested method is significant as it provides practical flood evacuation information effectively and intuitively, and the generated cartograms as empirical results also provide helpful insights for more efficient evacuation plans.

Louvre Efficient Evacuation Plan Based on LSFFM

For large tourist attraction Louvre, it is essential to establish an emergency evacuation plan for personnel in critical situations. We propose an effective graph-based path planning model, which is suitable for limited space with dense visitors flow. This paper analyze individual’s movement pattern with CA model, regional crowd flow, and overall suboptimal path planning. We establish a limited space fast flow model (LSFFM). Then we apply this evacuation model to the Louvre compared with other out of order evacuation condition. It shows a great performance for evacuation efficiency and visitors’ safety.

The best plan of evacuation in Louvre

According to reports, since 2012, there have been 12 terrorist attacks in France. At the same time, the number of visitors of Louvre reached a peak of 8.1 million in 2017. Therefore, making an efficient evacuation plan in Louvre is particularly important. In our paper, we assume that the process of evacuation contains two main parts: the path from the starting point to the exit, and the location where the congestion may occur, such as stairs and exits. For the first part, we use the ant colony optimization algorithm to find the optimal path and get how many flows arrive at each exit. For the second part, as we already know the number of flows, we can use queuing theory to determine whether it is congested or not and calculate the corresponding congestion time. Finally, the optimal evacuation path and the corresponding time are obtained. In addition, associated with the application "Affluences" used in Louvre, some devices are designed to measure the average speed of the crowd movement. If the average speed is below a threshold value, an emergency exit will be opened to improve the evacuation process.

Model and Solution for Non-conservation Flow Evacuation Planning Problem

Efficient evacuation plan with which a maximum evacuees can be sent as soon as possible from the disastrous place to the safe place is an important notion during the response phase of the disaster management. Such a plan in terms of optimization models has been extensively studied in a various scenarios, see [3]. The optimization models have been based on the flow conservation constraint which permits an evacuee to be taken out of the disastrous place only if it can be sent into the safe place. However, the evacuation plan model with no flow conservation can keep several evacuees in the relatively safe places besides the evacuees which could be sent into the safe place.&#x0D; In this paper, we describe an optimization model for the evacuation plan based on the non-conservation flow constraint with an efficient solution procedure which keeps a maximum evacuees on the prioritized intermediate places besides a maximum evacuees into the specified safe place.